Portfolio

DwellAssured Service Provider Coverage

DwellAssured was a startup that operated in the senior care and aging-in-place industry. Its core service was providing home safety assessments and recommendations for older adults who wanted to remain in their homes rather than move to assisted living.

The company connected clients with qualified service providers—such as home modification specialists, handymen, and healthcare professionals—to implement tailored solutions that helped seniors live safely and independently.

Issue:
DwellAssured assumed it had strong service provider coverage in the DC/VA/MD area, but this belief was not supported by any centralized or accurate data. Internally, teams operated under the false assumption that no coverage gaps existed. In reality, provider data was outdated, incomplete, and scattered across systems, making it impossible to confidently assess service reach or plan strategic expansion.

Solution:
To address these visibility and alignment challenges, a three-part solution was implemented:

1. Standardized Provider Survey – A detailed online form was sent to all active service providers to capture up-to-date service offerings and geographic reach (zip codes, cities, counties).

2. Centralized Provider Data – Survey responses were cleaned and standardized in Excel, tagged by service type and region to enable consistent analysis.

3. Geographic Mapping & Analysis – Using Power BI, Google Maps, and GIS tools, provider data was visualized through heatmaps and overlays to highlight dense areas and expose rural or underserved zones.

Result:
This solution replaced assumptions with real data, aligned cross-functional teams, exposed critical service gaps, and enabled leadership to make informed expansion decisions. It also established a repeatable process for scaling into new markets.

This project exemplifies how data-driven analysis can reveal the gap between organizational assumptions and operational reality. By systematically collecting, consolidating, and visualizing provider network data, I created a strategic asset that transformed decision-making from assumption-based to evidence-based, ultimately enabling more effective market expansion and resource allocation strategies.

Cyclistic Bike-Share Case Study

Google Data Analytics Professional Certificate

As part of the Google Data Analytics Professional Certificate, I completed the Cyclistic Bike-Share Case Study, which focused on understanding customer behavior for a fictional Chicago-based bike-share company. The objective was to analyze how casual riders differ from annual members and identify strategies to increase annual memberships.

Issue:

Cyclistic wanted to increase the number of annual memberships. While many riders used single-ride or day passes, these casual riders were less likely to commit long-term. The challenge was to understand how casual riders differ from annual members and identify strategies to encourage conversion.

Solution:

I analyzed a full year of historical trip data to uncover behavioral patterns between the two rider types. Using Excel and Tableau, I cleaned, organized, and visualized the data to identify key trends in ride frequency, duration, and location. My analysis revealed that casual riders were more active on weekends and during warmer months, while members rode more consistently throughout the week for shorter, routine trips. Based on these insights, I recommended targeted marketing efforts such as weekend promotions, discounted annual memberships, and app-based incentives for repeat casual riders.

Result:

The proposed strategy provided a data-driven framework to convert casual riders into annual members by aligning marketing initiatives with rider behavior patterns. The analysis also offered Cyclistic a clearer understanding of customer segments, supporting more efficient and personalized outreach strategies.